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NASA NTRS2008

Services, Perspective and Directions of the Space Physics Data Facility

The multi-mission data and orbit services of NASA's Space Physics Data Facility (SPDF) project offer unique capabilities supporting science of the Heliophysics Great Observatory and that are highly complementary to other services now evolving in the international heliophysics data environment. The VSPO (Virtual Space Physics Observatory) service is an active portal to a wide rage of distributed data sources. CDAWeb (Coordinated Data Analysis Web) offers plots, listings and file downloads for current data from many missions across the boundaries of missions and instrument types. CDAWeb now includes extensive new data from STEREO and THEMIS, plus new ROCSAT IPEI data, the latest data from all four TIMED instruments and high-resolution data from all DE-2 experiments. SSCWeb, Helioweb and out 3D Animated Orbit Viewer (TIPSOD) provide position data and identification of spacecraft and ground conjunctions. OMNI Web, with its new extension to 1- and 5-minute resolution, provides interplanetary parameters at the Earth's bow shock. SPDF maintains NASA's CDF (Common Data Format) standard and a range of associated tools including format translation services. These capabilities are all now available through web services based APIs, one element in SPDF's ongoing work to enable heliophysics community development of Virtual discipline Observatories (e.g. VITMO). We will demonstrate out latest data and capabilities, review the lessons we continue to learn in what science users need and value in this class of services, and discuss out current thinking to the future role and appropriate focus of the SPDF effort in the evolving and increasingly distributed heliophysics data environment.

McGuire, Robert E.↗

NASA NTRS2018

Demonstration of a 600 Watt Hybrid Iodine-Xenon Electric Propulsion System

This paper reviews recent iodine compatible electric propulsion technologies evaluated at the NASA Glenn Research Center. The work culminated in a 1,174-hour hybrid iodine-xenon propulsion system durability demonstration (iodine fed thruster with xenon fed cathode). The test demonstrated that (i) a Hall-effect thruster operates with nearly identical performance whether employing iodine or xenon propellant, (ii) careful selection of propulsion system materials and coatings can result in durable iodine-compatible hardware, and (iii) implementation of appropriate facility improvements and procedures can limit negative impacts of iodine on test hardware and ground support equipment. The work was motivated by advancements in capability of very dense small-spacecraft (<500 kg), but spacecraft still lacking very dense in-space propulsion to provide the large delta-v capabilities (3 to 10 km/s) required for many high-value NASA exploration and science missions. Volume limitations of small-spacecraft not only require dense propulsion systems, but more so dense propellants. NASA identifies xenon and iodine as having both favorable storage densities and propulsive properties to enable many NASA small-spacecraft mission scenarios. While xenon is inert and well-proven in spaceflight applications, iodine has triple the storage density of xenon and stores at low pressures, permitting use of conformal tank designs. Unlike xenon, iodine does raise concerns related to its reactivity with most materials, potential spacecraft-propellant interactions, impact on ground test facilities, and challenges to acceptance test iodine propulsion systems prior to flight. This work begins to address these challenges. This work was conducted under the Advanced In-Space Propulsion (AISP) project funded through the Game Changing Development (GCD) program within NASA's Science Technology Mission Directorate (STMD).

Benavides, G. F.↗

DOE OSTI2024

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1- and 5-min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1- and 5-min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hr in advance) in a large storm (SYM-H = -393 nT) using 5-min resolution data. When predicting the SYM-H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2010

The Kepler DB, a Database Management System for Arrays, Sparse Arrays and Binary Data

The Kepler Science Operations Center stores pixel values on approximately six million pixels collected every 30-minutes, as well as data products that are generated as a result of running the Kepler science processing pipeline. The Kepler Database (Kepler DB) management system was created to act as the repository of this information. After one year of ight usage, Kepler DB is managing 3 TiB of data and is expected to grow to over 10 TiB over the course of the mission. Kepler DB is a non-relational, transactional database where data are represented as one dimensional arrays, sparse arrays or binary large objects. We will discuss Kepler DB's APIs, implementation, usage and deployment at the Kepler Science Operations Center.

McCauliff, Sean↗

NASA NTRS2009

Recent Electric Propulsion Development Activities for NASA Science Missions

(The primary source of electric propulsion development throughout NASA is managed by the In-Space Propulsion Technology Project at the NASA Glenn Research Center for the Science Mission Directorate. The objective of the Electric Propulsion project area is to develop near-term electric propulsion technology to enhance or enable science missions while minimizing risk and cost to the end user. Major hardware tasks include developing NASA s Evolutionary Xenon Thruster (NEXT), developing a long-life High Voltage Hall Accelerator (HIVHAC), developing an advanced feed system, and developing cross-platform components. The objective of the NEXT task is to advance next generation ion propulsion technology readiness. The baseline NEXT system consists of a high-performance, 7-kW ion thruster; a high-efficiency, 7-kW power processor unit (PPU); a highly flexible advanced xenon propellant management system (PMS); a lightweight engine gimbal; and key elements of a digital control interface unit (DCIU) including software algorithms. This design approach was selected to provide future NASA science missions with the greatest value in mission performance benefit at a low total development cost. The objective of the HIVHAC task is to advance the Hall thruster technology readiness for science mission applications. The task seeks to increase specific impulse, throttle-ability and lifetime to make Hall propulsion systems applicable to deep space science missions. The primary application focus for the resulting Hall propulsion system would be cost-capped missions, such as competitively selected, Discovery-class missions. The objective of the advanced xenon feed system task is to demonstrate novel manufacturing techniques that will significantly reduce mass, volume, and footprint size of xenon feed systems over conventional feed systems. This task has focused on the development of a flow control module, which consists of a three-channel flow system based on a piezo-electrically actuated valve concept, as well as a pressure control module, which will regulate pressure from the propellant tank. Cross-platform component standardization and simplification are being investigated through the Standard Architecture task to reduce first user costs for implementing electric propulsion systems. Progress on current hardware development, recent test activities and future plans are discussed.

Pencil, Eric J.↗

NASA NTRS1969

A study of evaluation techniques for lunar science Final report

Analysis for quantifying value of scientific experiments in lunar exploration program

Source record↗

DOE OSTI2022

Volcanology, Geochemistry, and Petrology Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

This article is composed of a commentary about the state of Integrated, Coordinated, Open, and Networked (ICON) principles in Volcanology, Geochemistry, and Petrology (VGP), and discussion on the opportunities and challenges of adopting them. VGP encompasses a broad field that addresses volcanic, magmatic, hydrothermal, geomicrobial systems; process investigations that span the physical, geochemical and biological realms, including planetary geology; and one that is extensively supported by state-of-the-art research facilities. We suggest that an open, inclusive, collaborative and evolving model of an international coordinated network is critical to answering the most pressing challenges in VGP. In this commentary piece, we begin to discuss the elements of, challenges to, and path forward in developing such a model. For this team, ICON means collaboration, equitable access to data for the entire scientific community, and forging of partnerships that potentially contribute to more innovative ways of coordinating and sharing research. It also means bringing more equity to science, by implementing effective measures which consider access to funding, analytical equipment, resources, and mentors. More importantly, ICON to us means having important conversations around what we value in the advancement of science, perhaps exploring outside the idea of meritocracy and evaluating what individual traits can contribute to science outside what has traditionally been considered the norm.

58 GEOSCIENCES↗

NASA NTRSDate not supplied

Visualizations to Aid Decision-Making in the ACCP Value Framework

The Aerosols, Clouds, Convection, and Precipitation Value Framework is used to introduce structure, transparency, and traceability into the decision-making process for a study formulating and assessing potential observing system concepts to respond to the 2017-2027 Decadal Survey for Earth Science and Applications. Within the Value Framework, visualizations play an important role in presenting information and structuring conversations around that information. This paper describes three guiding principles that were applied to the design of the visualizations of the Value Framework, presents examples of visualizations that serve at least one of several functions (structuring communication, facilitating elicitation and aggregation of data, and summarizing complex information), and identifies lessons learned from the development and implementation of those visualizations. The guiding principles and lessons learned have not only contributed to the success of the Value Framework and the ACCP study, but also can be applied in other decision-making activities.

Christopher A. Jones↗

NASA NTRSDate not supplied

Visualizations to Aid Decision-Making in the ACCP Value Framework

Christopher A Jones↗

NASA NTRS1972

Revised standards for the solar constant and the solar spectrum

The values of the solar constant and solar spectrum are presented which are proposed as revised standards for science and technology. A solar constant value of 1.94 cal/sq cm min or 135.3 mW/sq cm was proposed. A detailed spectral irradiance table is given for the range of 0.12 to 1000 microns.

Thekaekara, M. P.↗

DOE OSTI2020

Plant science decadal vision 2020–2030: Reimagining the potential of plants for a healthy and sustainable future

Abstract Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2014

NASA GeneLab Concept of Operations

NASA's GeneLab aims to greatly increase the number of scientists that are using data from space biology investigations on board ISS, emphasizing a systems biology approach to the science. When completed, GeneLab will provide the integrated software and hardware infrastructure, analytical tools and reference datasets for an assortment of model organisms. GeneLab will also provide an environment for scientists to collaborate thereby increasing the possibility for data to be reused for future experimentation. To maximize the value of data from life science experiments performed in space and to make the most advantageous use of the remaining ISS research window, GeneLab will apply an open access approach to conducting spaceflight experiments by generating, and sharing the datasets derived from these biological studies in space.Onboard the ISS, a wide variety of model organisms will be studied and returned to Earth for analysis. Laboratories on the ground will analyze these samples and provide genomic, transcriptomic, metabolomic and proteomic data. Upon receipt, NASA will conduct data quality control tasks and format raw data returned from the omics centers into standardized, annotated information sets that can be readily searched and linked to spaceflight metadata. Once prepared, the biological datasets, as well as any analysis completed, will be made public through the GeneLab Space Bioinformatics System webb as edportal. These efforts will support a collaborative research environment for spaceflight studies that will closely resemble environments created by the Department of Energy (DOE), National Center for Biotechnology Information (NCBI), and other institutions in additional areas of study, such as cancer and environmental biology. The results will allow for comparative analyses that will help scientists around the world take a major leap forward in understanding the effect of microgravity, radiation, and other aspects of the space environment on model organisms. These efforts will speed the process of scientific sharing, iteration, and discovery.

Space Life Science↗

NASA NTRS1997

WFPC2 photometry from subtraction of TinyTim PSFs

Based upon the subtraction of TinyTim Point Spread Function (PSFs) from Principle Component Image (PC1) point-like objects, a method has been developed to determine the optimal values for the telescope jitter and the Z4 relative focus during calibration or science observations. Using these jitter and focus values, an optimal TinyTim PSF, computed over a resampled grid, is then iteratively fitted to the object, yielding an improvement in the PSF centering, more accurate photometric results and a better detection of underlying structures. Preliminary results seem to indicate that appropriate synthetic TinyTim PSFs perform as well as observed PSFs.

Remy, M.↗

NASA NTRSDate not supplied

Visualizations to Aid Decision-Making in the ACCP Value Framework

NASA’s priorities for Earth Science are informed by the 2017-2027 Decadal Survey for Earth Science and Applications from Space of the National Academies of Sciences, Engineering and Medicine. In that document, five Designated Observables are identified as priorities for research, two of which are Aerosols, and Clouds, Convection, and Precipitation. NASA is addressing these two Designated Observables by a combined study (the Aerosols, Clouds, Convection, and Precipitation, or ACCP, study). In this context of the ACCP study’s multiple objectives and multiple stakeholders, heuristic-based approaches are insufficient to assess candidate observing system concepts due to the complexity of the decision problem. For the ACCP study, this complexity in assessment is such that absent a structured approach that permits understanding of the steps leading to a decision, the decision risks being made on an incomplete basis. While prose and numeric data both have their place in effective communication, visualizations play an important part not only in presenting information, but also in structuring conversations around that information. Each of the visualizations developed for the ACCP Value Framework serve at least one of several functions: it structures communication about a method or process, it facilitates the elicitation and aggregation of data, or it summarizes complex information to enable analysis. Thus, these visualizations can present information, structure conversations, or do both.

Christopher A Jones↗

DOE OSTI2020

Characterization of long period return values of extreme daily temperature and precipitation in the CMIP6 models: Part 1, model evaluation

Not Available

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

NASA NTRS2017

Mining Twitter Data to Augment NASA GPM Validation

The Twitter data stream is an important new source of real-time and historical global information for potentially augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. There have been other similar uses of Twitter, though mostly related to natural hazards monitoring and management. The validation of satellite precipitation estimates is challenging, because many regions lack data or access to data, especially outside of the U.S. and in remote and developing areas. The time-varying set of "precipitation" tweets can be thought of as an organic network of rain gauges, potentially providing a widespread view of precipitation occurrence. Twitter provides a large source of crowd for crowdsourcing. During a 24-hour period in the middle of the snow storm this past March in the U.S. Northeast, we collected more than 13,000 relevant precipitation tweets with exact geolocation. The overall objective of our project is to determine the extent to which processed tweets can provide additional information that improves the validation of GPM data. Though our current effort focuses on tweets and precipitation, our approach is general and applicable to other social media and other geophysical measurements. Specifically, we have developed an operational infrastructure for processing tweets, in a format suitable for analysis with GPM data; engaged with potential participants, both passive and active, to "enrich" the Twitter stream; and inter-compared "precipitation" tweet data, ground station data, and GPM retrievals. In this presentation, we detail the technical capabilities of our tweet processing infrastructure, including data abstraction, feature extraction, search engine, context-awareness, real-time processing, and high volume (big) data processing; various means for "enriching" the Twitter stream; and results of inter-comparisons. Our project should bring a new kind of visibility to Twitter and engender a new kind of appreciation of the value of Twitter by the science research communities.

validatio↗

NASA NTRS2021

Ambient Performance Testing of the CubeSat Infrared Atmospheric Sounder (CIRAS)

Hyperspectral infrared measurements of Earth’s atmosphere from space have proven their value for weather forecasting, climate science and atmospheric composition. The CubeSat Infrared Atmospheric Sounder (CIRAS) instrument will demonstrate a fully functional infrared temperature, water vapor and carbon monoxide sounder in a CubeSat sized volume for at least an order of magnitude lower cost than legacy systems. Design for a CubeSat significantly reduces cost of access to space and enables flight in a constellation to reduce revisit time and enable new measurements including 3D winds. A technology demonstration of CIRAS is currently under development at JPL. The effort has completed integration and ambient testing of a high fidelity brassboard, complete with the flight configured optics assembly developed by Ball Aerospace with a JPL Immersion Grating and Black Silicon Entrance Slit. The brassboard includes a flight-configured High Operating Temperature Barrier Infrared Detector (HOT-BIRD) mounted in an Integrated Dewar Cryocooler Assembly (IDCA), enabling testing in the ambient environment. Ambient testing included radiometric testing of the system to characterize the instrument operability and NEdT. Spatial testing was performed to characterize the system line spread function (LSF) in two axes and report FWHM of the LSF. Spectral testing involved an air path test to characterize the spectral/spatial transformation matrix, and an etalon was used to measure the Spectral Response Functions (SRFs). Results of the testing show the CIRAS performs exceptionally well and meets the key performance required of the system. The end result of testing is the CIRAS instrument now meets TRL 4 with confidence in a brassboard configuration ready for thermal vacuum (TVac) testing necessary to achieve TRL 5 for the system.

Wilson, Robert C.↗